Sequential Product Positioning Under Differential Costs

Tyagi, Rajeev K.

doi:10.1287/mnsc.46.7.928.12038

Cited by 92 publications

(44 citation statements)

References 23 publications

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“…The customers were assumed to be differentiated either vertically or horizontally based on product attributes [2], [8], [12], [18], [29], [45], [56]. Customers are vertically differentiated if all customers agree that the attribute of differentiation is the quality, and that customers differ in their willingness to pay more for higher quality.…”

Section: Literature Reviewmentioning

confidence: 99%

Selecting a Customization Strategy Under Competition: Mass Customization, Targeted Mass Customization, and Product Proliferation

Cavusoglu

Raghunathan

2007

IEEE Trans. Eng. Manage.

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Abstract-Customization requires not only an implementation of proper manufacturing systems but also a proper strategy regarding when firms should offer customized products and what the nature of customization should be. This paper questions 1) whether customization is better than no customization, and, if so, 2) what kind of customization strategy firms should adopt under competition. We find that customization is not optimal when the cost of soliciting customer preference information is sufficiently high. When competing firms choose to customize, we show that firms target only certain customer segments with customized products. We also find that the optimal customization strategy may require firms to offer only a few discrete product varieties. Despite the concern that customization may initiate price wars because customization reduces product differentiation, we find that customization does not escalate the price competition, because aggressive price competition exacerbates cannibalization. Although customers within the product line of a firm are charged higher prices, we show that on average customers are better off when firms adopt customization. However, unless the customization is quite cheap, when firms choose to customize, we find that firms cannot generate more profits than when firms offer only a single product.

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Section: Literature Reviewmentioning

confidence: 99%

Selecting a Customization Strategy Under Competition: Mass Customization, Targeted Mass Customization, and Product Proliferation

Cavusoglu

Raghunathan

2007

IEEE Trans. Eng. Manage.

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“…Other papers explore competition in quality choice followed by price competition (e.g., Moorthy 1988, Jones and Mendelson 1997, Chambers et al 2006. Tyagi (2000) investigates duopoly competition in sequential product positioning under horizontal differentiation. We consider competition under limited capacities.…”

Section: Literature Reviewmentioning

confidence: 99%

Feasting on Leftovers: Strategic Use of Shortages in Price Competition Among Differentiated Products

Porteus

Shin

Tunca

2010

M&SOM

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T wo single-product firms with different quality levels and fixed limited capacities engage in sequential price competition in an essentially deterministic model where customers have heterogeneous valuations for both products. We develop conditions under which the leader (she) can take strategic advantage of her limited capacity by pricing relatively low, purposefully creating shortages and leaving some leftovers for the follower (him) to feast on, avoiding direct competition. The extent to which the leader benefits in this Leftovers Equilibrium depends on operational variables such as the capacity levels of the two firms and the sequence in which customers arrive at the market. We spell out the details for three different known arrival sequences within a specific subset of plausible fixed-capacity levels. The follower's strategic shadow price can be positive even when not all his capacity is used, and the leader's can be negative when all her capacity is used. We illustrate that Leftovers Equilibria can arise when some of our assumptions are relaxed.

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“…Simple models to determine the most profitable characteristics of a single new product [1-2] have progressed to account for issues such as product-line design and preference heterogeneity [3][4][5][6][7], competitor reactions [8-10], cost structure [11][12], distribution channels [9,13-16], choice-set-dependent preferences [17], and coordination with constrained engineering design decisions [18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Robust Design for Profit Maximization Under Uncertainty of Consumer Choice Model Parameters Using the Delta Method

Resende

Heckmann

Michalek

2011

Volume 5: 37th Design Automation Conference, Parts a and B

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In new product design, risk averse firms must consider downside risk in addition to expected profitability, since some designs are associated with greater market uncertainty than others. We propose an approach to robust optimal product design for profit maximization by introducing an α-profit metric to manage expected profitability vs. downside risk due to uncertainty in market share predictions. Our goal is to maximize profit at a firm-specified level of risk tolerance. Specifically, we find the design that maximizes the α-profit: the value that the firm has a (1-α) chance of exceeding, given the distribution of possible outcomes. The parameter α [0,1] INTRODUCTIONOver the last three decades, a significant portion of the new product development (NPD) literature has been dedicated to the integration of engineering design and marketing processes for differentiated markets. Simple models to determine the most profitable characteristics of a single new product [1-2] have progressed to account for issues such as product-line design and preference heterogeneity [3][4][5][6][7], competitor reactions [8-10], cost structure [11][12], distribution channels [9,13-16], choice-set-dependent preferences [17], and coordination with constrained engineering design decisions [18][19][20][21][22][23][24][25][26].As Hsu and Wilcox [27] argue, the trend towards estimating marketing models at lower levels of aggregation that are more structural 1 in consumer behavior representation has led to models with many parameters and consequently greater uncertainty of those parameters. However, despite the advances in NPD methods, the research has not given much consideration to the intrinsic parameter uncertainty of the demand models. Demand uncertainty directly affects the risk of introducing a new product into the market, and firms evaluate potential projects not only in terms of expected return, but also in terms of risk.The purpose of this work is threefold. First, we define a robust a-profit metric and propose a general framework to incorporate demand uncertainty arising from choice model parameter estimation into the design decision process such that it accounts for varying levels of loss tolerance. Second, we apply the delta method to approximate the a-profit function in closed form for multinomial logit (MNL) demand models to be used efficiently in numerical optimization routines. Finally, we show how ignoring demand uncertainty can lead to suboptimal decisions for risk averse firms.We do not intend to consider all the various sources of demand model uncertainty [28], and several questions will remain open. In particular, we assume the discrete choice model is correctly specified and ignore uncertainty due to model misspecification, and we assume that the model parameters do not change over time or from the context in which the data were collected to the context in which predictions will be made. Nevertheless, the proposed methodology can be useful, and it serves as a first step in addressing design for profit maximization...

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Sequential Product Positioning Under Differential Costs

Cited by 92 publications

References 23 publications

Selecting a Customization Strategy Under Competition: Mass Customization, Targeted Mass Customization, and Product Proliferation

Selecting a Customization Strategy Under Competition: Mass Customization, Targeted Mass Customization, and Product Proliferation

Feasting on Leftovers: Strategic Use of Shortages in Price Competition Among Differentiated Products

Robust Design for Profit Maximization Under Uncertainty of Consumer Choice Model Parameters Using the Delta Method

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